Impact of sleep disorder treatment on fatigue in multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: We recently reported that sleep disorders are significantly associated with fatigue in multiple sclerosis (MS). OBJECTIVE: The objective of this paper is to assess the effects of sleep disorder treatment on fatigue and related clinical outcomes in MS. METHODS: This was a controlled, non-randomized clinical treatment study. Sixty-two MS patients completed standardized questionnaires including the Fatigue Severity Scale (FSS), Multidimensional Fatigue Inventory (MFI), Epworth Sleepiness scale (ESS) and Pittsburgh Sleep Quality Index (PSQI), and underwent polysomnography (PSG). Patients with sleep disorders were offered standard treatment. Fifty-six subjects repeated the questionnaires after ≥ three months, and were assigned to one of three groups: sleep disorders that were treated (SD-Tx, n=21), sleep disorders remaining untreated (SD-NonTx, n=18) and no sleep disorder (NoSD, n=17). RESULTS: FSS and MFI general and mental fatigue scores improved significantly from baseline to follow-up in SD-Tx (p <0.03), but not SD-NonTx or NoSD subjects. ESS and PSQI scores also improved significantly in SD-Tx subjects (p <0.001). Adjusted multivariate analyses confirmed significant effects of sleep disorder treatment on FSS (-0.87, p = 0.005), MFI general fatigue score (p = 0.034), ESS (p = 0.042) and PSQI (p = 0.023). CONCLUSION: Treatment of sleep disorders can improve fatigue and other clinical outcomes in MS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it